Wang Jiaping
Laboratory Medicine, Donghai County People's Hospital, Lianyungang City, Jiangsu 222300, China.
Math Biosci Eng. 2021 Jun 8;18(5):5125-5145. doi: 10.3934/mbe.2021261.
Cutaneous melanoma (SKCM) is the most invasive malignancy of skin cancer. Metastasis to distant lymph nodes or other system is an indicator of poor prognosis in melanoma patients. The aim of this study was to identify reliable prognostic biomarkers for SKCMs.
Four RNA-sequencing datasets associated with SKCMs were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database as well as corresponding clinical information. Differentially expressed genes (DEGs) were screened between primary and metastatic samples by using MetaDE tool. Weighted gene co-expression network analysis (WGCNA) was conducted to screen functional modules. A prognostic score (PS)-based predictive model and nomogram model were constructed to identify signature genes and independent clinicopathologic factors.
Based on MetaDE analysis and WGCNA, a total of 456 overlapped genes were identified as hub genes related to SKCMs progression. Functional enrichment analysis revealed these genes were mainly involved in the hippo signaling pathway, signaling pathways regulating pluripotency of stem cells, pathways in cancer. In addition, eight optimal DEGs (RFPL1S, CTSV, EGLN3, etc.) were identified as signature genes by using PS model. Cox regression analysis revealed that pathologic stage T, N and recurrence were independent prognostic factors. Three clinical factors and PS status were incorporated to construct a nomogram predictive model for estimating the three years and five-year survival probability of individuals.
The prognosis prediction model of this study may provide a promising method for decision making in clinic and prognosis predicting of SKCM patients.
皮肤黑色素瘤（SKCM）是皮肤癌中侵袭性最强的恶性肿瘤。远处淋巴结或其他系统转移是黑色素瘤患者预后不良的指标。本研究旨在确定SKCM可靠的预后生物标志物。
从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库下载了四个与SKCM相关的RNA测序数据集以及相应的临床信息。使用MetaDE工具筛选原发样本和转移样本之间的差异表达基因（DEG）。进行加权基因共表达网络分析（WGCNA）以筛选功能模块。构建基于预后评分（PS）的预测模型和列线图模型，以识别特征基因和独立的临床病理因素。
基于MetaDE分析和WGCNA，共鉴定出456个重叠基因作为与SKCM进展相关的核心基因。功能富集分析表明，这些基因主要参与河马信号通路、调节干细胞多能性的信号通路、癌症中的信号通路。此外，使用PS模型将八个最佳DEG（RFPL1S、CTSV、EGLN3等）鉴定为特征基因。Cox回归分析表明，病理分期T、N和复发是独立的预后因素。纳入三个临床因素和PS状态，构建列线图预测模型，以估计个体的三年和五年生存概率。
本研究的预后预测模型可能为SKCM患者的临床决策和预后预测提供一种有前景的方法。
